This post is a quick reminder that the next Cologne R user group meeting is only one week away. We will meet on 6 July 2012. The meeting will kick off at 18:00 with three short talks at the Institute of Sociology and will continue, even more informal, from 20:00 in a pub (LUX) nearby.

All details are available on the KölnRUG Meetup site. Please sign up if you would like to come along. Notes from the first Cologne R user group meeting are available here.

Their research was based on electrophysiological experiments carried out in the late 1940s and early 1950 on a giant squid axon to understand how action potentials in neurons are initiated and propagated.

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This evening I will talk about Dynamical systems in R with simecol at the LondonR meeting.

Thanks to the work by Thomas Petzoldt, Karsten Rinke, Karline Soetaert and R. Woodrow Setzer it is really straight forward to model and analyse dynamical systems in R with their deSolve and simecol packages.

I will give a brief overview of the functionality using a predator-prey model as an example.

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Transforming data sets with R is usually the starting point of my data analysis work. Here is a scenario which comes up from time to time: transform subsets of a data frame, based on context given in one or a combination of columns.

As an example I use a data set which shows sales figures by product for a number of years:

I am interested in absolute and relative sales developments by product over time. Hence, I would like to add a column to my data frame that shows the sales figures divided by the total sum of sales in each year, so I can create a chart which looks like this:

There are lots of ways of doing this transformation in R. Here are three approaches using:

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A new version of googleVis has been released on CRAN and the project site. Version 0.2.16 adds the functionality to plot quarterly and monthly data as a motion chart.

To illustrate the new feature I looked for a quarterly data set and stumbled across the quarterly UK house price data published by Nationwide, a building society. The data is available in a spread sheet format and presents the average house prices and indexed to 100 in Q1 1993 by region in the UK from Q4 1973 to Q1 2012. Unfortunately the data is formated for human eyes rather than for computers, see the screen shot below.

Screen shot of Nationwide's UK house price data in Excel

Never-mind, the XLConnect package by Mirai Solutions does a fabulous job in reading Excel files into R. An advantage of XLConnect, compared to other packages, is that it also works on a Mac, although I had to install the package from source (install.packages("XLConnect", type="source")).